Amirreza Parya
Data-driven modelling of an Aquifer Thermal Energy Storage (ATES) using machine learning.
Rel. Alessandro Casasso, Riccardo Taormina, Martin Bloemendal. Politecnico di Torino, Corso di laurea magistrale in Petroleum And Mining Engineering (Ingegneria Del Petrolio E Mineraria), 2023
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Abstract
In the face of climate change and the imperative to reduce greenhouse gas emissions, harnessing geothermal energy for mitigating environmental impact and curbing global warming stands as a promising solution. The increasing reliance on intermittent renewable energy resources necessitates effective energy storage solutions for both electricity and heat. Underground reservoirs provide a substantial avenue for heat storage, predominantly achieved through Aquifer Thermal Energy Storage (ATES) systems. Interestingly, the widespread adoption of ATES remains limited, with the Netherlands hosting nearly 90% of global ATES installations. Furthermore, the majority of existing ATES systems predominantly store low-temperature heat (below 30°C). Enhancing their efficiency and energy density hinges on the storage of higher-temperature water during warm seasons for subsequent heat recovery in colder periods.
This study investigates the potential of the Long Short-Term Memory (LSTM) deep learning approach to forecast the well temperatures at the ATES of a horticultural facility of Koppert-Cress, in the Netherlands
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